Opportunities for information technologies and knowledge management to answer emerging challenges for manufacturing and services industries in the digital economy

Pablos, Patricia Ordóñez de; Lytras, Miltiadis D.; Visvizi, Anna; Zhang, Xi

doi:10.1002/hfm.20786

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…According to practitioners, the development of start-ups has a high degree of uncertainty (Blank, 2006(Blank, , 2013Ries, 2011). Furthermore, diffusion processes of new business innovations have become increasingly complicated and multifaceted in recent years (de Pablos et al, 2019). In this high uncertainty situation, start-ups are very vulnerable to failure.…”

Section: Lean Start-up As Start-up Innovation Frameworkmentioning

confidence: 99%

Determinant factors of lean start-up-based incubation metrics on post-incubation start-up viability: case-based study

Tritoasmoro

Ciptomulyono²,

Dhewanto³

et al. 2022

JSTPM

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Purpose This paper aims to investigate the effect of business incubation metrics based on an adaptation of the lean start-up (LS) framework on start-up survival after incubation. This study also analyzes the obstacles in implementing the LS framework as incubation metrics. Design/methodology/approach This study uses mixed methods. Quantitative research using multiple linear regression was applied to the data of 30 start-ups incubated at Bandung Techno Park for the 2014–2017 period and survival tracking data after the incubation. A qualitative approach to complete the explanatory work was conducted through in-depth interviews with 12 respondents, including start-up graduates from the incubation program, program managers and mentors. Findings This study confirms that several LS incubation metrics significantly affect start-up sustainability after incubation. In addition, this study also explains several problems in applying the LS discipline that needs attention to increase incubation success. Research limitations/implications Research was conducted only at one technology business incubator (TBI) model that focuses on digital start-ups in the emerging ecosystem. Research results can be biased in different situations and ecosystems. Practical implications The explanation of the relationship of LS-based incubation metrics to the survival of start-ups, as well as the challenges of their implementation, can be a reference for TBI management to consider and prioritize intervention strategies, thereby improving TBI’s business processes and increasing the success rate of incubated start-ups. Social implications The creation of university start-ups and spin-offs has become a key performance indicator mandatory for technology universities in Indonesia. The existence of TBI institutions in universities as channels of technology commercialization is essential. The incubator’s success in creating a new technology-based company will have a significant social impact on the surrounding environment. Originality/value Although the LS method is popular in start-up communities and among practitioners, it is rarely used in the incubation process at universities. These results can be considered for university TBIs to explore LS as an incubation management tool to increase the success rate of incubated start-ups.

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Section: Lean Start-up As Start-up Innovation Frameworkmentioning

confidence: 99%

Determinant factors of lean start-up-based incubation metrics on post-incubation start-up viability: case-based study

Tritoasmoro

Ciptomulyono²,

Dhewanto³

et al. 2022

JSTPM

View full text Add to dashboard Cite

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“…Some scholars have discussed the impact of AI on scientific research, the expansion of scientific discoveries, and the economic impact of AI in last few years [42,43]. Visvizi, Lytras, and co-workers conducted research on smart cities, big data, education, knowledge management, and AI, explored the comprehensive impact of related technology and service, and discussed the relationship between policy-making and academic research, focusing on existing research and technological innovation [44][45][46][47]. In recent years, AI has gradually become a national strategy in China, and relevant policies and designs at the central and local levels have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019

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In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009)(2010)(2011)(2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013)(2014)(2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016)(2017)(2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.abstract symbols using logical rules) was the most productive way to pursue a computer that could "think"; however, as originally unimagined by Turing and others, the strategy of constructing an inference engine did not achieve the initial cognitive tasks, and it seemed that the theoretically possible concepts had not produced many feasible applications in practice [1]. Li and Wang gave five common definitions of artificial intelligences (AI) in their book Artificial Intelligence: (1) AI is a computer program which makes people feel inconceivable; (2) AI is a computer program similar to human thinking;(3) AI is a computer program similar to human behavior; (4) AI is a computer program that can learn; (5) AI is a computer ...

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“…The use of information and communication technologies, different tools, and knowledge management strategies in organizations can lead to innovation. These innovations occur in services, products, and processes, and maintain competitive advantages in globalized economies around the world [1].…”

Section: Introductionmentioning

confidence: 99%

Virtual Training Application by Use of Augmented and Virtual Reality under University Technology Enhanced Learning in Slovakia

et al. 2019

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The use of modern information and communication technologies plays a key role in the current global competitive environment of long-term manufacturing company sustainability. Based on the requirements of Slovak industrial companies (both domestic and foreign companies), there is a need to implement these technologies into the university teaching process. Therefore, this article describes virtual and augmented reality applications into the teaching process of Digital Factory module at the Industrial Engineering Department of the University of Zilina. The main aim of this study was to test and document how helpful immersive technologies could be in a teaching process. There were a group of students who took part in this study whose job was to assemble a specific assembly construction, first without the help of virtual technologies and second with the help of augmented and virtual reality. The results were collected through operation images and chronometry, these were then analyzed the conclusions stated in the article were obtained. In the conclusion, the final part of the article describes a general evaluation of virtual training use in the industrial practice of long-term industrial company sustainability.

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Opportunities for information technologies and knowledge management to answer emerging challenges for manufacturing and services industries in the digital economy

Cited by 3 publications

References 6 publications

Determinant factors of lean start-up-based incubation metrics on post-incubation start-up viability: case-based study

Determinant factors of lean start-up-based incubation metrics on post-incubation start-up viability: case-based study

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

Virtual Training Application by Use of Augmented and Virtual Reality under University Technology Enhanced Learning in Slovakia

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